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Capacity Estimation of Signalized Intersections Considering Connected Automated Vehicle Observability.

Ruochuan Fan1,2,3, Jian Lu1,2,3

  • 1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
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Summary

Connected automated vehicles (CAVs) improve signalized intersection capacity. This study presents a new method to estimate capacity with mixed traffic and varying CAV levels, showing nonlinear increases in capacity.

Keywords:
CAV penetrationcapacity assessmentconnected and automated vehiclessignalized intersectiontraffic state prediction

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Area of Science:

  • Transportation Engineering
  • Traffic Flow Theory
  • Intelligent Transportation Systems

Background:

  • Connected automated vehicles (CAVs) enhance intersection observability.
  • Existing capacity models fail with mixed traffic and varying CAV penetration.

Purpose of the Study:

  • Develop a quantitative capacity estimation method for signalized intersections under mixed traffic.
  • Provide a baseline for evaluating and predicting intersection operations with increasing CAV penetration.

Main Methods:

  • Improved CAV gain parameter and car-following states to derive equivalent headways.
  • Continuous estimation of intersection capacity based on CAV penetration.
  • Analysis of capacity and saturation trends using real-world intersection data.

Main Results:

  • Intersection capacity increases nonlinearly with rising CAV penetration.
  • Distinct growth rates observed for straight-through and left-turn movements.
  • Method enables continuous capacity estimation across different traffic demands.

Conclusions:

  • The proposed method offers an engineering-oriented reference for capacity estimation.
  • Accurate traffic state prediction is achievable under mixed traffic conditions.
  • Results highlight the significant impact of CAVs on intersection performance.